Local Post-hoc Explainable Methods for Adversarial Text Attacks Article Swipe
Yidong Chai
,
Ruicheng Liang
,
Hongyi Zhu
,
Sagar Samtani
,
Meng Wang
,
Yezheng Liu
,
Yuanchun Jiang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.17185568.v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.17185568.v1
Deep learning models have significantly advanced various natural language processing tasks. However, they are strikingly vulnerable to adversarial text attacks, even in the black-box setting where no model knowledge is accessible to hackers. Such attacks are conducted with a two-phase framework: 1) a sensitivity estimation phase to evaluate each element’s sensitivity to the target model’s prediction, and 2) a perturbation execution phase to craft the adversarial examples based on estimated element sensitivity. This study explored the connections between the local post-hoc explainable methods for deep learning and black-box adversarial text attacks and proposed a novel eXplanation-based method for crafting Adversarial Text Attacks (XATA). XATA leverages local post-hoc explainable methods (e.g., LIME or SHAP) to measure input elements’ sensitivity and adopts the word replacement perturbation strategy to craft adversarial examples. We evaluated the attack performance of the proposed XATA on three commonly used text-based datasets: IMDB Movie Review, Yelp Reviews-Polarity, and Amazon Reviews-Polarity. The proposed XATA outperformed existing baselines in various target models, including LSTM, GRU, CNN, and BERT. Moreover, we found that improved local post-hoc explainable methods (e.g., SHAP) lead to more effective adversarial attacks. These findings showed that when researchers constantly advance the explainability of deep learning models with local post-hoc methods, they also provide hackers with weapons to craft more targeted and dangerous adversarial attacks.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.36227/techrxiv.17185568.v1
- https://www.techrxiv.org/articles/preprint/Local_Post-hoc_Explainable_Methods_for_Adversarial_Text_Attacks/17185568/1/files/31864979.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200456679
All OpenAlex metadata
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- OpenAlex ID
-
https://openalex.org/W4200456679Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.36227/techrxiv.17185568.v1Digital Object Identifier
- Title
-
Local Post-hoc Explainable Methods for Adversarial Text AttacksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-21Full publication date if available
- Authors
-
Yidong Chai, Ruicheng Liang, Hongyi Zhu, Sagar Samtani, Meng Wang, Yezheng Liu, Yuanchun JiangList of authors in order
- Landing page
-
https://doi.org/10.36227/techrxiv.17185568.v1Publisher landing page
- PDF URL
-
https://www.techrxiv.org/articles/preprint/Local_Post-hoc_Explainable_Methods_for_Adversarial_Text_Attacks/17185568/1/files/31864979.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.techrxiv.org/articles/preprint/Local_Post-hoc_Explainable_Methods_for_Adversarial_Text_Attacks/17185568/1/files/31864979.pdfDirect OA link when available
- Concepts
-
Adversarial system, Computer science, Hacker, Post hoc, Craft, Deep learning, Artificial intelligence, Sensitivity (control systems), Black box, Robustness (evolution), Machine learning, Computer security, Engineering, Geography, Chemistry, Archaeology, Gene, Electronic engineering, Dentistry, Medicine, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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62Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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